{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized!\n",
      "Hello David !\n",
      "Good-bye David !\n"
     ]
    }
   ],
   "source": [
    "class Man:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        print(\"Initialized!\")\n",
    "    def hello(self):\n",
    "        print(\"Hello\", self.name, \"!\")\n",
    "    def goodbye(self):\n",
    "        print(\"Good-bye\", self.name, \"!\")\n",
    "        \n",
    "m = Man(\"David\")\n",
    "m.hello()\n",
    "m.goodbye()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "print(*X[1])\n",
    "X[X > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0, 6, 0.1)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, label=\"sin\")\n",
    "plt.plot(x, y2, linestyle=\"--\", label=\"cos\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"sin & cos\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = imread(\"python.png\")\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
